With the release of OctoPerf’s new UI we wanted to create a component that would allow our users to easily edit HTTP request actions. The new UI being heavily inspired by IDEs such as Eclipse or Visual Studio we decided to create a component that behaves likes the project settings panel of IntelliJ: IntelliJ Settings This panel displays a tree on its left part with a search input on top. The content of the left part changes depending on the current selection.

BivwAk! is the transformation hub for BNP Paribas. Its goal is to bring new services and skillsets at the crossroad of all BNP Paribas entities. It’s in this context that Dino learned about OctoPerf. Dino Dona is the QA lead at BivwAk! He handles all the testing-related activities. Dino is very passionate about testing because it's a constantly evolving space. With new methodologies and tools emerging all the time. We met Dino at the french testing event JFTL.

We are going to look at scalability testing in this Blog Post. On paper scalability testing is simple because all you are doing is increasing the load, but there are some common pitfalls that can be easily avoided. So you need to really understand what you are trying to achieve before you start.

We are going to explore whether Load Test Driven Development is an idea that would be worth pursuing for your organisation. We will recap on what Test-Driven Development (TDD) is in the next section but fundamentally Now when you think about this, does it make sense to try and run a performance test before we have developed any code? We think it does and we are going to explain why. For clarity we are not suggesting that Load test Driven Development should replace TDD but rather to compliment it.

It’s been a while since the last update post in july 2021, not that we haven’t updated OctoPerf since then but the additions we’ve made are not easy to share in a blog. Allow me to take an example.

In this Blog Post we are going to look at the GraphQL HTTP Request sampler and look at how GraphQL requests can also be made using a HTTP Request sampler in case you are for some reason restricted to an earlier version of JMeter (the sampler was only introduced in JMeter 5.4). We will also look at some of the principles of GraphQL.

Building performance tests that conform to a very specific level of load and concurrency is a standard approach to performance testing. You determine your peak levels of load and concurrency, and you build a test that meets this. You build soak test and scalability tests that conform to pre-determined levels of load and concurrency, and you execute these alongside the other scenarios you build to meet your performance requirements.

Pearson, founded in the 19th century, is one of the world leaders in providing education services all around the globe. Francisco Muniz is the Performance Architect for Pearson, responsible for Performance Alignment across Pearson’s Virtual Learning. This position entails working with many different parts of the organization, such as Architecture, Development, and QA. As such, Francisco was leading and overseeing the important Octoperf and Loadrunner software switching project.

There are many samplers that JMeter provides but I am willing to bet that the HTTP Request samplers is the most frequently used and, in this Blog Post, we are going to look at how this works and how it can be configured.

This post does not look at a particular aspect of JMeter nor does it give a detailed overview of how to use a particular tool that will compliment your performance testing with JMeter. What it is about is the principles of push to production pipelines and performance testing and while I have stated that this post is not specifically about JMeter in my experience JMeter is one of the best performance testing tools for this type of pipeline integration.